Localization using WiFi Signal Strength

As WiFi access points become increasingly prevalent in the urban
environment, it is natural to ask if these access points may be
exploited for purposes other than communication. In this project, we
are exploring the use of WiFi signal strength information as
a cue for localizing mobile robots. The basic process is as
follows.

First, we construct a WiFi signal-strength map of the environment
using a combination of sampling and interpolation. Samples are
gathered by one or more robots; these robots are equipped with
scanning laser range finders and use a standard MCL algorithm for
localization [?]. The figure below, for example,
shows the WiFi signal-strength map generated for the second floor of
the USC SAL building (there are four access points on this floor, only
three of which are shown in the signal-strength map).

Note that WiFi signal strength does not follow a parametric
(inverse-square) model in indoor environments.

Second, we construct a sensor model for WiFi signal
strength, taking into account sensor noise and environmental
variation; we expect the signal strength map to change somewhat as
people move through the environment, doors are opened or closed, and
so on.

Equipped with both the signal strength map and model, robots
employ a standard MCL algorithm to localize themselves. The figure
below, for example, shows a particle filter converging on the correct
robot pose (green dots show the WiFi-based estimate).

Click here for an
animation of this experiment. Empirically, we have found that robots
may be localized to within 50cm using only WiFi signal strength and
odometry.